1/16 Design of Intelligent Human Interface - Brief Summary of Research Activities - Yasufumi Takama Tokyo Institute of Technology ( Currently: Tokyo Metropolitan.

Slides:



Advertisements
Similar presentations
GMD German National Research Center for Information Technology Darmstadt University of Technology Perspectives and Priorities for Digital Libraries Research.
Advertisements

Retrieval of Information from Distributed Databases By Ananth Anandhakrishnan.
Chapter 5: Introduction to Information Retrieval
TAILS: COBWEB 1 [1] Online Digital Learning Environment for Conceptual Clustering This material is based upon work supported by the National Science Foundation.
Designing Multimedia with Fuzzy Logic Enrique Diaz de Leon * Rene V. Mayorga ** Paul D. Guild *** * ITESM, Guadalajara Campus, Mexico ** Faculty of Engineering,
Web Mining Research: A Survey Authors: Raymond Kosala & Hendrik Blockeel Presenter: Ryan Patterson April 23rd 2014 CS332 Data Mining pg 01.
Unified Modeling Language
Information Retrieval in Practice
DSS: Decision Support Systems and AI: Artificial Intelligence
Interfaces for Selecting and Understanding Collections.
Supervised by Prof. LYU, Rung Tsong Michael Department of Computer Science & Engineering The Chinese University of Hong Kong Prepared by: Chan Pik Wah,
INFO 624 Week 3 Retrieval System Evaluation
A Topic Specific Web Crawler and WIE*: An Automatic Web Information Extraction Technique using HPS Algorithm Dongwon Lee Database Systems Lab.
Reference Collections: Task Characteristics. TREC Collection Text REtrieval Conference (TREC) –sponsored by NIST and DARPA (1992-?) Comparing approaches.
ITCS 6010 Natural Language Understanding. Natural Language Processing What is it? Studies the problems inherent in the processing and manipulation of.
EXPERT SYSTEMS Part I.
Computer comunication B Information retrieval Repetition Retrieval models Wildcards Web information retrieval Digital libraries.
DSS: Decision Support Systems and AI: Artificial Intelligence
Lecture #1COMP 527 Pattern Recognition1 Pattern Recognition Why? To provide machines with perception & cognition capabilities so that they could interact.
1 / 26 CS 425/625 Software Engineering Architectural Design Based on Chapter 11 of the textbook [SE-8] Ian Sommerville, Software Engineering, 8t h Ed.,
Overview of Long-Term Memory laura leventhal. Reference Chapter 14 Chapter 14.
12 -1 Lecture 12 User Modeling Topics –Basics –Example User Model –Construction of User Models –Updating of User Models –Applications.
Overview of Search Engines
Intelligent Systems Lecture 23 Introduction to Intelligent Data Analysis (IDA). Example of system for Data Analyzing based on neural networks.
CS598CXZ Course Summary ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign.
Challenges in Information Retrieval and Language Modeling Michael Shepherd Dalhousie University Halifax, NS Canada.
An Object-Oriented Approach to Programming Logic and Design
Introduction To Computer System
Information Need Question Understanding Selecting Sources Information Retrieval and Extraction Answer Determina tion Answer Presentation This work is supported.
System Concepts & Components Dr. Dania Bilal IS582 Spring 2009.
Database System Concepts and Architecture
Introduction to Database Systems
Defining Text Mining Preprocessing Transforming unstructured data stored in document collections into a more explicitly structured intermediate format.
School of Computing FACULTY OF ENGINEERING Developing a methodology for building small scale domain ontologies: HISO case study Ilaria Corda PhD student.
Chapter 2 Architecture of a Search Engine. Search Engine Architecture n A software architecture consists of software components, the interfaces provided.
Knowledge Representation and Indexing Using the Unified Medical Language System Kenneth Baclawski* Joseph “Jay” Cigna* Mieczyslaw M. Kokar* Peter Major.
Digital Image Processing & Analysis Spring Definitions Image Processing Image Analysis (Image Understanding) Computer Vision Low Level Processes:
Web Searching Basics Dr. Dania Bilal IS 530 Fall 2009.
Data Mining Chapter 1 Introduction -- Basic Data Mining Tasks -- Related Concepts -- Data Mining Techniques.
Carnegie Mellon School of Computer Science Copyright © 2001, Carnegie Mellon. All Rights Reserved. JAVELIN Project Briefing 1 AQUAINT Phase I Kickoff December.
Ontario Ministry of Education, SS/L-18ITEB 2009 Differentiated Instruction Summer Program 1 Student Success/Learning to 18 DI Summer Program 2010 MODULE.
Subtask 1.8 WWW Networked Knowledge Bases August 19, 2003 AcademicsAir force Arvind BansalScott Pollock Cheng Chang Lu (away)Hyatt Rick ParentMark (SAIC)
Artificial Intelligence Research Center Pereslavl-Zalessky, Russia Program Systems Institute, RAS.
ES component and structure Dr. Ahmed Elfaig The production system or rule-based system has three main component and subcomponents shown in Figure 1. 1.Knowledge.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-1 Chapter 3 Decision Support Systems:
Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology Mining Logs Files for Data-Driven System Management Advisor.
1 Wichtige Aspekte des eLearning Hermann MAURER Technische Universität Graz Präsentation für die Universität Graz
Chapter 4 Decision Support System & Artificial Intelligence.
Introduction to Information Retrieval Example of information need in the context of the world wide web: “Find all documents containing information on computer.
Digital Libraries1 David Rashty. Digital Libraries2 “A library is an arsenal of liberty” Anonymous.
Jane Reid, AMSc IRIC, QMUL, 30/10/01 1 Information seeking Information-seeking models Search strategies Search tactics.
Chapter 10. The Explorer System in Cognitive Systems, Christensen et al. Course: Robots Learning from Humans On, Kyoung-Woon Biointelligence Laboratory.
An Ontology-Based Intelligent Information System for Urbanism and Civil Engineering Stefan Trausan-Matu, Anca Neacsu “ Politehnica" University of Bucharest.
Digital Video Library Network Supervisor: Prof. Michael Lyu Student: Ma Chak Kei, Jacky.
Chapter 5: MULTIMEDIA DATABASE MANAGEMENT SYSTEM ARCHITECTURE BIT 3193 MULTIMEDIA DATABASE.
Natural Language Processing Group Computer Sc. & Engg. Department JADAVPUR UNIVERSITY KOLKATA – , INDIA. Professor Sivaji Bandyopadhyay
Computer Vision Group Department of Computer Science University of Illinois at Urbana-Champaign.
BIT 3193 MULTIMEDIA DATABASE CHAPTER 5 : MULTIMEDIA DATABASE MANAGEMENT SYSTEM ARCHITECTURE.
Agents for Case-based software reuse Stein Inge Morisbak Web:
1 CS 430: Information Discovery Lecture 26 Architecture of Information Retrieval Systems 1.
5. 2Object-Oriented Analysis and Design with the Unified Process Objectives  Describe the activities of the requirements discipline  Describe the difference.
Smart Web Search Agents Data Search Engines >> Information Search Agents - Traditional searching on the Web is done using one of the following three: -
General Architecture of Retrieval Systems 1Adrienn Skrop.
A Self-organizing Semantic Map for Information Retrieval Xia Lin, Dagobert Soergel, Gary Marchionini presented by Yi-Ting.
Organization of Information LSIS Summer II (2005)
Perspectives on Information Course Introduction January 25, 2016.
From LSE-30: Observatory System Spec.
Architecture Components
Object oriented system development life cycle
CSE 635 Multimedia Information Retrieval
Presentation transcript:

1/16 Design of Intelligent Human Interface - Brief Summary of Research Activities - Yasufumi Takama Tokyo Institute of Technology ( Currently: Tokyo Metropolitan Institute of Technology) PREST, Japan Science and Technology Corporation

2/16 Characteristics of VSA Multi-modal Interface Face-to-face interaction & speech dialog Connected with WWW browser Suitable for users with little knowledge of computers Applicable to facility guidance systems Dialog management system (DMS) with learning module Acquisition of cooperative answering strategy VSA 1

3/16 DMS with Learning Module Cooperative answer depends on both user ’ s knowledge & system ’ s location E.g. “ Where is the library? ” Tell the way from system ’ s location to the library Use the landmark that user seems to know 2 types of knowledge acquisition Overlay-type user-model to estimate the knowledge state of the present user Reinforcement learning to acquire location- specific appropriate rules VSA 2

4/16 Reading documents Making diagrams Retrieving new documents Viewpoint-based support by Fisheye Matching Viewpoint extraction Viewpoint information feature generation Users’ Activity Document Ordering while Reading is an Effective Way of Dealing with Vast Collection of Documents FISH VIEW 1

5/16 From medical news Extracted Concepts as Viewpoints IDHeading InformationWords (stemmed) 3f98b3value of healthdiseas,sickne,health,etc component of living bodyprotei,immuno,choles,dna 30f6dainternal organseye,heart,lung,knee,etc. 3f969ediseasesyndro,aids,cancer,cold,etc cmedical suppliesdrug,medici,laxati,acid,etc. 30f6f7medical instrumentsbandag,cathet,glasse,etc. From cinema reviews Categories of action, SF, monster, etc. FISH VIEW 2

6/16 Clustering-based visualization applied to WWW-IR Categorization (static, i.e. user independent) Naive clustering (one-shot, i.e. no inheritance) K-means, STC, etc. Query Network: Plastic Clustering for Visualization Plastic clustering (gradual formation through a series of IR) Query Network 1

7/16 Analogy to Immune Network Antibodies generated for antigens Activated antibody can be sensitive to next invasion Various antibodies in plastic structure Clusters generated for retrieved documents Effective cluster can be reused for subsequent retrieval Various clusters in plastic structure Activation value calculation based on differential equation of mathematical biology Query Network 2